cproxyme {factormodel} | R Documentation |
cproxyme
Description
This function estimates a linear factor model using continuous variables. The linear factor model to estimate has the following form. proxy = intercept + factorloading * (latent variable) + measurement error The measurement error is assumed to follow a Normal distribution with a mean zero and a variance, which needs to be estimated.
Usage
cproxyme(dat, anchor = 1, weights = NULL)
Arguments
dat |
A proxy variable data frame list. |
anchor |
This is a column index of an anchoring proxy variable. Default is 1. That is, the code will use the first column in dat data frame as an achoring variable. |
weights |
An optional weight vector |
Value
Returns a list of 3 components :
- alpha0
This is a vector of intercepts in a linear factor model. The k-th entry is the intercept of k-th proxy variable factor model.
- alpha1
This is a vector of factor loadings. The k-th entry is the factor loading of k-th proxy variable. The factor loading of anchoring variable is normalized to 1.
- varnu
This is a vector of variances of measurement errors in proxy variables. The k-th entry is the variance of k-th proxy measurement error. The measurement error is assumed to follow a Normal distribution with mean 0.
- mtheta
This is a mean of the latent variable. It is equal to the mean of the anchoring proxy variable.
- vartheta
This is a variance of the latent variable.
Author(s)
Yujung Hwang, yujungghwang@gmail.com
References
- Cunha, F., Heckman, J. J., & Schennach, S. M. (2010)
Estimating the technology of cognitive and noncognitive skill formation. Econometrica, 78(3), 883-931. doi: 10.3982/ECTA6551
- Hwang, Yujung (2021)
Bounding Omitted Variable Bias Using Auxiliary Data. Working Paper.
Examples
dat1 <- data.frame(proxy1=c(1,2,3),proxy2=c(0.1,0.3,0.6),proxy3=c(2,3,5))
cproxyme(dat=dat1,anchor=1)
## you can specify weights
cproxyme(dat=dat1,anchor=1,weights=c(0.1,0.5,0.4))